Optimal dispatching control of an AGV in a JIT production system

This paper deals with an FMS (Flexible Manufacturing System) in a JIT (Just-In-Time) production system. The FMS consists of m workstations, one dispatching station and a single AGV (Automated Guided Vehicle). Each workstation has an input buffer of limited capacity and its processing times are distributed stochastically. When the processing of a new component starts at the workstation, a withdrawal Kanban attached to it is sent to the dispatching station. The AGV chooses one from workstations whose withdrawal Kanbans are accumulated at the dispatching station, and conveys a component with a withdrawal Kanban from the dispatching station to the workstation. The main purpose of this paper is to find an optimal dispatching policy of the AGV that maximizes the long-run expected average reward per unit time. The problem is formulated as a semi-Markov decision process and an optimal dispatching policy is computed. Numerical experiments are performed to make several comparisons.     

What are Relevant Work Incentive Models? Shirking Model, Gift Exchange Model, or Reciprocity Model

This empirical study examines the relevance of different work incentive models by distinguishing reciprocity induced by pecuniary and non-pecuniary factors. Although the literature does not present an examination of different models all together but rather specifically examines each model mainly through experimental laboratory games, we study which models are suitable to explain work incentives in real labor markets. Our analyses reveal that overall reciprocity induced by non-pecuniary factors motivates employees well. The shirking model induces effort only if employees face a high level of vulnerability to firing. However, the high level of vulnerability to dismissal invalidates the gift exchange and reciprocity models. These results can complement controlled laboratory experiments, whose results are often restricted in applying to the reality.     

Selection and reproductive success in males of the dragonfly,Orthetrum japonicum (Odonata: Libellulidae)

Reproductive success, copulation success, and mating success were measured for a population of male dragonflies,Orthetrum japonicum. Copulation success explained the greatest variation in reproductive success. The proportion of copulations followed by oviposition was positively correlated with the number of oviposited eggs per mating. Directional selection on four morphological characters was estimated. The effect of selection on correlated traits was comparable to that of direct selection. Directional selection varied between traits and between episodes in a single trait. The probability that the observed directional selection on the four morphological traits was expected under the condition of the selective neutrality of traits was not smaller than 5%.     

Stock return predictability: A factor-augmented predictive regression system with shrinkage method

To predict stock market behaviors, we use a factor-augmented predictive regression with shrinkage to incorporate the information available across literally thousands of financial and economic variables. The system is constructed in terms of both expected returns and the tails of the return distribution. We develop the variable selection consistency and asymptotic normality of the estimator. To select the regularization parameter, we employ the prediction error, with the aim of predicting the behavior of the stock market. Through analysis of the Tokyo Stock Exchange, we find that a large number of variables provide useful information for predicting stock market behaviors.     

DECOMPOSITION ALGORITHM FOR PERFORMANCE EVALUATION OF AGV SYSTEMS

This paper proposes a realistic queueing model of automated guided vehicle (agv) systems in just-in-time production systems. The model takes into consideration return paths, Erlang distributed service times, and pull-type dispatching rule, assuming finite buffer capacities. Since it has no product-form solution and natural decomposability due to complex nontree fork-cum-join architecture and dynamic dispatching rules, we propose a machine-based decomposition algorithm for the performance evaluation of the model. Each decomposed module consists of the processing machine and its dispatching station. Three flow probabilities, derived from flow conservation analysis, relate the modules, which are updated iteratively until the parameters converge. The numerical results from a real-life Agv system application show that the algorithm is reasonably accurate.